RETRACTED ARTICLE: Soft electronic material based sensor with optical network in sports application for player movement analysis using machine learning model

Image detection technology may also be used to collect data about players and sports industry activities, which is especially relevant given that the vast majority of current athletic events are televised either live or as video files. The current state of affairs, however, makes it clear that technology for identifying sports images is still in its infancy. This study uses sports footage as its data source to investigate the use of image recognition techniques in the realm of sports. This research proposes a novel method for assessing player movement in electronic materials with soft sensors via feature extraction and categorization using machine learning models and optical networks. In this case, data was collected by means of an optical network-based soft sensor image of player movement, which was then subjected to normalisation and noise reduction. The properties of the input image were then retrieved using a Gaussian Q-Boltzmann neural network with classification based on a radial multilayer vector kernelization transfer model. Experimental investigation is done in terms of training accuracy, average precision, recall, scalability, and quality-of-service based on sensor image analysis using soft sensor electronic material analysis. This study has concurrently constructed a test platform to demonstrate the usefulness of this research approach and generated tools for recognising athletes, distinguishing motion, rating sports conduct, etc.